AN OBSERVATORY OF LAND SURFACE TEMPERATURE CHANGES BY LAND
USE AND LAND COVER CHANGES OF YANGON MEGA CITY, MYANMAR
Khin Mar Yee, Hoyong Ahn, Dongyoon Shin, Jinwoo Park, Hohyun Jeong, Chuluong Choi
Department of Spatial Information Engineering, Pukyong National University
[email protected], [email protected], [email protected]
[email protected], [email protected] [email protected]
KEY WORDS: Land use and Land cover Change (LULCC), Land Surface Temperature (LST), RS and GIS
ABSTRACT: Yangon Mega City is densely populated and most urbanization area of Myanmar. Rapid urbanization
is the main causes of Land Use and Land Cover Change (LULCC) and their impact on Land Surface Temperature
(LST). The objectives of this study were to investigate on the LST with respect to LULC of Yangon Mega City . For
this research, Landsat satellite images of 1996, 2006 and 2014 of Yangon Area were used. Supervised classification
with the region of interest and calculated change detection. Ground check points used 348 points for accuracy
assessment. The overall accuracy is 89.94 percent. The result of this paper, the vegetation area is decreased from
24.5 % in 1996 to 11.2 % in 2014 and built up area is clearly increased from 11.2 % in 1996 to 33.1 % in 2014. The
land surface temperature increase in built up area, cultivation and bare land and decrease in vegetation and water area.
The results of the image processing is pointed out that land surface temperature ranged from 23º C, 26º C and 27º C
to 36º C, 42º C and 43.3º C for three periods within 19 years period. The findings of this paper revealed a notable
land use and land cover change and land surface temperature for the future sustainable urban planning of Yangon
Mega City.
1. INTRODUCTION
Around 50% (or 3.5 billion) of world population is now living in urban areas, which is expected to be 60% (4.9
billion) by 2030 (Mutizwa-Mangiza N.D, et al., 2011) and this percentage will reach 69.6% by 2050 (United Nation,
2010). This increasing trend in global urbanization influenced many researchers to investigate the potential impacts
of man-made activities on urban thermal environment such as the LST and UHI effect (Ye H, et al., 2010). Unplanned
and haphazard urbanization coupled with the poor building design are the biggest causes of heat islands in cities.
Research has shown that UHI is primarily caused by the built environment in urban areas, in which natural areas are
replaced with non-permeable and high temperature surface of concrete and asphalt (Jones P.D, et al., 1990). Cities
are characterized by increased air and surface temperatures as compared to their rural surroundings. This so called
urban heat island (UHI) effect is caused by the specific urban structure, the set of physical features which can be
described by land-use (LU) patterns and other structural indicators, such as the degree of surface sealing (Thinh, Arlt,
Heber, Hennersdorf, & Lehmann, 2002). Land use and land cover (LULC) changes induced by human or natural
processes drive biogeochemistry of the earth influencing the climate at global as well as regional scales. Land use
land cover (LULC) information is essential for managing natural resources and monitoring of environmental changes.
(Setturu B, Rajan KS, Ramachandra TV, 2013).
Currently, urbanization is considered the most important driver of climate change (McCarthy, Best, & Betts, 2010).
As addressed in previous studies, intensive and rapid urbanization is an example of human-induced land use and land
cover (LULC) change, which has exacerbated the ongoing impacts on the climate system (Jin, Dickinson, & Zhang,
2005). Land use/land cover (LULC) change associated with urbanization is the essential cause of global change and
often results in remarkable urban heat island (UHI) effect, which will influence the regional climate and
socioeconomic development. Numerous programs have been devoted to studies of LULC change, UHI effects and
possible mitigation strategies (Su and Yang, 2007). Land surface temperature observations acquired by remote
sensing technologies have been used to assess the UHI, to develop models of land surface atmosphere exchange, and
to analyze the relationship between temperature and land use and land cover changes in urban areas (Voogt & Oke,
2003) recent studies have addressed the relationship between LST and surface characteristics such as vegetation
indices (Carlson et al., 1994; Owen, et al., 1998). Remote sensing is extremely useful for understanding the
spatiotemporal land cover change in relation to the basic physical properties in terms of the surface radiance and
emissivity data. Since the 1970s, satellite derived (such as Landsat Thematic Mapper-TM) surface temperature data
have been utilized for regional climate analyses on different scale (Tran, H, Uchihama, D, Ochi, S, and Yasuoka, Y,
2006 and Carlson, T.N, Augustine, J. A, and Boland, F.E, 1977). Accurate land surface emissivity values aid in
reliable inferences among different land covers for retrieving LST from thermal infrared (TIR) data (Ramachandra
TV, Uttam Kumar, 2010, Becker F, Li ZL, 1995 and Dash P, Gottsche FM, Olesen FS, Fischer H, 2005). Qualitative
studies on the correlation between LULC and land surface temperature (LST) help us in appropriate land use planning
and UHI mitigation (Chen and Jim, 2003).
The topic of climate change has become the great interest both among the academicians and governing bodies at
present. Many researchers try to find out the various aspect of this phenomenon including its causes, impacts and
complexity. The same thing is improving population and changing urbanization is main driver of land use and land
cover change and it is effect on the land surface temperature, seriously effect especially urban and its surrounding
area. Nowadays, most of the cities area are facing with the urban micro climate and changing their environment.
IPCC focus on the global temperature will be 1˚ C to 3.5˚ C by the end of the 21th century. Although the various
changing temperature, some cities of Southeast Asia will be 4˚ C and 5˚ C (IPCC, 2002).
Based on the above discussion, the main focus of this paper is to study on the serious changes of the land use and
land cover and land surface temperature of the Yangon Mega City area, Myanmar. The objectives of this paper is to
examine the changes of land use and land cover with change detection by using visible and near infrared bands, to
measure the changes of the land surface temperature with thermal bands, to analysis on the relationship between the
land use and land cover changes and land surface temperature changes and sustainable development for future urban
planning and urban microclimate.
Yangon Mega city is former capital of Myanmar. It lies between 16˚ 35' to 17 ˚ 06' N latitude and between 96 ˚ 00'
and 96 ˚ 25' E longitude (Figure 1). This area is located 34 km upstream from the mouth of Ynaogn River and an
average height of 30 m and degenerates gradually into delta plain in eastwards and westwards. Yangon is still the
largest urban agglomeration and the most densely populated area in Myanmar. It is the home of more than 7.3 million
(according to 2014 census) people with the total area of approximately 4333.45 square kilometer. This area is
attracting a huge amount of rural-urban migrants from all over the country because of the opportunity for job,
education, health and daily life facilities etc. The population of this city has increased by about over double time in
the past two decades. Due to these population explosion mainly due to rural-urban migration and partially due to
natural growth, Yangon is expanding both vertically and horizontally. As discussed earlier, these expansions have
been identified to be the major contributors of LST increase.
Figure 1. Location of Study Area
2. DATA AND METHODOLOGY
Landsat satellite images (1996, 2006 and 2014) were downloaded from the official website of US Geological Survey
(USGS) and used in order to reach the research objectives. The data used in the study includes Landsat 5 data (path
132, row 48) for 1996, Landsat 7 ETM+ data (path 132, row 48) for 2006 and Landsat 8 (OLI) data (path 132, row
48) for 2014 with cloudless area. Spatial resolution is 30 x 30 m. UTM zone is 47 and Datum is WGS 84 Table 1.
Total ground check points are 348 points (58 points for each LULC). The climate data of the ground station is
Kabaraye station, Yangon City from 1953 to 2013 Table 2. The procedures of this paper consist of three phases. The
first phase is land cover classification with training sample and calculation of change area by change detection of
three periods. The second phase is retrieval of the land surface temperature of 1996, 2006 and 2014. The third phase
is the data analysis of land use and land cover and land surface temperature changes. Figure 2 outlines the procedure
adopted in the current study.
Satellite Image 1996
Satellite Image 2006
Satellite Image 2014
Multispectral Band
Thermal Inferred Band
Ratified and Georeferenced
Radiance
At-Sensor Brightness Temperature
Supervised Classification
Accuracy Assessment
Emissivity Effect
Change Detection
LST Map
LULC Map
Analysis
Data
Processing
Comparison of Temporal and Spatial Change
Results
Figure 2. Research Flow
Table 1. Details of Satellite Image used
Respective Year
Date Acquired
Sensor
1996
1996/03/25
Landsat 4-5 Thematic Mapper TM
2006
2006/02/25
Landsat 7 Enhanced Thematic Mapper Plus
(ETM+) Thematic Mapper TM
2014
2014/03/27
Landsat 8 OLI/ TIRS
Table 2. Temperature Data of Yangon (1963-2013)
Year
J
F
M
A
M
J
J
A
S
O
N
D
1963
22.8
23.5
25.3
29.1
28.5
27.9
26.6
24.3
24.7
25.5
25.1
23.9
1973
22.7
24.7
25.5
29.4
28.7
28.6
26.6
25.7
24.5
25.7
24.9
23.3
1983
23.6
24.7
25.9
30.4
28.9
28.9
26.6
26.1
26.7
25.5
25.1
23.5
1993
24.4
25.8
26
30.6
29.9
28.9
26.6
26.3
26.8
25.6
24.8
24.2
2003
24.9
28.5
29.6
31.1
30.7
29.9
28.1
27.9
26.9
27.5
26.1
25.1
2013
25.8
27.5
29.6
30.7
30.5
29.7
27.6
27.7
27.7
27.65
27.1
25.9
2.1 Derivation of LULC
Land use and land cover changes (LULCC) are complex processes involving multiple driving forces that are location
specific and context dependent. Land use land cover changes are also spatially and temporally dynamic. The using
satellite images were classified into six classes of land use and land cover types, as shown in Table 3. The chosen
colour composite for Landsat 5, 7 (band 4, 3 and 2) and Landsat 8 (band 5, 4 and 3) were used with training sample
sites for land cover classification. The training sites developed for this research were based on the reference data and
ancillary information collected from various sources. For the making map of land cover classification, the land use
and land cover pattern was mapped by supervised classification with the support vector machine classification
algorithm of ENVI 5.1 and ArcMap 10.2.2.
Table 3. Description of LULC Type
Land cover Type
Description
River and Stream
Water body of river and stream area
Lake and Pond
Water body of permanent open water, lakes, ponds, canals, seasonal wetland, low
lying areas, mushy land, and swamps
Vegetation
Trees, natural vegetation, mixed forest, gardens, gardens, parks and playgrounds,
grassland, vegetated lands
Cultivated land
Crops fields, pastures, orchards, vineyards and nurseries
Built up area
All infrastructure-residential, commercial mixed use and industrial areas, villages,
settlements, road network, pavements, and man-made structures
Scatter Vegetation and mixed vegetation which has a scattered distribution, mostly
shrubs and rangeland
Bare
land
Table 4. LULC of Mega City Yangon (1996, 2006 and 2014)
LULC
1996
2006
2014
(sq km)
%
(sq km)
%
(sq km)
%
River & stream
170.19
3.9
179.73
4.1
153.08
3.5
Lake and pond
54.99
1.3
63.53
1.5
69.5
1.6
Vegetation
1061.08
24.5
566.2
13.1
483.53
11.2
Cultivated land
2525.33
58.3
2403.45
55.4
2154.54
49.7
Built up area
485.33
11.2
1066.01
24.6
1435.72
33.1
Bare land
36.53
0.8
54.51
1.3
37.09
0.9
The classified images were then assessed for accuracy based on a random selection of 100 reference pixels for each
time period. Change detection is used to correlate and compare two sets of imagery to identify changes. Using change
detection statistics is to compile a detail tabulation of changes between three periods. The accuracies of the classified
images for 1996, 2006 and 2014 were, respectively, found to be 85.91 % for 1996, 89.08 % for 2006 and 94.82 %
for 2014. The total overall accuracy is 89.94 %.
2.2 Retrieval of LST
Land Surface Temperature is derived from geometrically corrected Landsat 5, Landsat 7 TM and ETM+ thermal
infrared (TIR) channel (band 6) and Landsat 8 OLI thermal infrared (TIR) channel (band 10 and 11). The
geometrically rectified images are free from distortions related to the sensors (e.g., jitter, view angle effect etc.,),
satellite (e.g., attitude deviations from nominal), and the Earth (e.g., rotation, curvature, relief) (Landsat 7 Science
Data Users Handbook, 2010).The Land Surface Temperature was measured with the individual thermal images and
were compared between different time periods. Based on the literature, different retrieval methods of brightness
temperature from the TM and ETM+ and OLI images were applied for the land surface temperature processing
(Landsat Project Science Office at NASA’s Goddard Space Flight Center: Greenbelt, MD, USA, 2010).
----(1)
Based on Chen et al., (2002), a two-step process was followed to derive brightness temperature from the Landst 5
TM images in this research (Chen, Y, Wang, J, Li, X, 2002) (Equation 1). The first step, the digital numbers (DNs)
of band 6 were converted to radiation luminance (RTM6) and the second step, the radiation luminance was converted
to at-satellite brightness temperature in Kelvin, T(K), using the following formula (Equation 2).
----(2)
K1 = 1260.56 K and K and K2 = 607.66 (mW x
cm -2 x sr -1 µm -1), which are pre-lunch calibration constants
under an assumption of unity emissivity; b represents effective spectral range, when the sensor’s response is much
more than 50%, b =1.239 µm ( Landsat 7 Science Data Users Handbook, 2010).
Retrieval of LST from the Landsat 7 ETM+ Images (LST for 2006) based on the literature (Chen Y; Wang J.; Li X,
2002 and Chen X-L, et al., 2006). The first step, the DNs of band 6 were converted to radiance and the second step
is the effective at-satellite temperature of the viewed Earth-atmosphere system, under the assumption of a uniform
emissivity, could be obtained by the following equation (3):
𝑹𝒂𝒅𝒊𝒂𝒏𝒄𝒆 =
𝑳𝑴𝑨𝑿 − 𝑳𝑴𝑰𝑵
𝑿 (𝑸𝑪𝑨𝑳 − 𝑸𝑪𝑨𝑳𝑴𝑰𝑵) + 𝑳𝑴𝑰𝑵
𝑸𝑪𝑨𝑳𝑴𝑨𝑿 − 𝑸𝑪𝑨𝑳𝑴𝑰𝑵
----(3)
The information can be obtained from the header file of the images, QCALMIN = 1, QCALMAX = 255, QCAL =DN,
and LMAX and LMIN are the spectral radiances for band 6 at digital numbers 1 and 255, respectively (Chen Y, Wang
J, Li X, 2002).
T=
K2
K1
In ( + 1)
Lλ
----(4)
T is the effective at-satellite brightness temperature in Kelvin; K1= 666.09 (watts/ (meter2 x ster x µm)) and K2 =
1282.71 (Kelvin) are calibration constants; and L is the spectral radiance in watts/ (meter2 x ster x µm) (Chen Y,
Wang J, Li X, 2002). (Equation 3 and 4). The value of top of atmospheric (TOA) spectral radiance was determined
by multiplicative rescaling factor (0.000342) of TIR bands with its corresponding TIR band and adding additive
rescaling factor (0.1) with it.
Retrieval of LST from the Landsat 8 OLI Images (LST for 2014) based on the following equation (5) and (6).
----(5)
Brightness temperature (TB) is the microwave radiation radiance traveling upward from the top of Earth's atmosphere.
The calibration process has been done for converting thermal DN values of thermal bands of TIR to TB. For finding
TB of an area the Top of Atmospheric (TOA) spectral radiance of (Lλ) was needed. TB for both the TIRs bands was
calculated by adopting the following formula,
----(6)
In this study, the image-based method was employed to retrieve Land Surface Temperature from Landsat TM/ETM
data due to its simplicity and validity and compared with the other frequently used algorithms, such as mono-window
algorithm (Qin, Karnieli, & Berliner, 2001) and the single channel algorithm (Jimenez-Munoz & Sobrino, 2003).
3. RESULTS AND DISCUSSION
3.1 Changes of LULC
The due to human activities results of land use and land cover classification of three period, the results of land use
and land cover classes describe in Figure 3 and the results of change is the change area of land use and land cover
classes of study area of 1996, 2006 and 2014.
Table 4. LULC Change area
LULC
1996-2006
sq km
%
9.54
0.2
8.54
Vegetation
2006-2014
%
sq km
%
-26.65
-0.6
-17.11
-0.4
0.2
5.97
0.1
14.51
0.3
-494.88
-11.4
-82.67
-1.9
-577.55
-13.3
Cultivated land
-121.88
-2.9
-248.91
-5.7
-370.79
-8.6
Built up area
580.68
13.4
369.71
8.5
950.39
21.9
Bare land
17.98
0.5
-17.42
-0.4
0.56
0.1
River and
stream
Lake and pond
sq km
1996-2014
Figure 3. Spatial Distribution of LULC (1996, 2006 and 2014)
According to change detection algorithm, vegetation, cultivated land decreased. Vegetation area decreased clearly
very much from 1061.08 to 566.2 square kilometer from 1996 to 2006 and decreased again to 483.53 square kilometer
in 2014. River and stream, lake and bare land are nearly same area within three periods. Built up area increased from
485.33 square kilometer to 1066.01 square kilometer between 1996 and 2006. In 2014, build up area raised again to
1435.72 square kilometer respectively. Cultivated land increased a little area from 2006 to 2014. The sample size
takes 58 points for ground check points of each land use and land cover class. The total 348 ground check points help
to calculate the accuracy percentage. The overall accuracy is over 89.94 % and shown in Figure 3 and Table 4.
3.2 Change in Land Surface Temperature
Figure 4 shows clearly the distribution and changes in the three periods of land surface temperature of Yangon Mega
City from 1996, 2006 and 2014 and changes of land surface temperature. The results of the image processing point
out that land surface temperature ranged from 23º C, 26 and 27º C to 36º C, 42º C and 43.3º C for three periods within
19 years.The built-up area and harvested cultivated land area were higher land surface temperature than the
surrounding area. Sparsely degraded forest area of land surface temperature is also higher temperature.
Figure 4. Spatial Distribution of LST (1996, 2006, 2014)
3.3 Relationship between LULC and LST
The land surface temperature was wider to the built up area, harvested cultivated area and bare land area. The different
levels of Land Surface Temperature located in the certain regions corresponding to the location of the land cover
classes. The high Land Surface Temperature located in the built up area, cultivated land and bare land. Low Land
Surface Temperature scattered the vegetation and water area. Figure 5 and 6 illustrate the dynamic changes of LULC
and LST situation.
Figure 5. Extension of Built up area between 1996, 2006 and 2014
The clearly extension of built up area to the northern side and eastern site of the study area because of better
transportation. Green or vegetation area is gradually decreased and replaced built up area and cultivated area.
According to increasing built up area, the temperature is wider and higher to the residential area including industrial
zone, cultivated land and bare land.
Figure 6. Increasing LST area for 1996, 2006 and 2014
4. CONCLUSIONS
The study investigated the relationships between the LULC and LST in Yangon Mega City. This paper pointed out
that land surface temperature is rising with the increasing human activities of land use and land cover changes. Results
indicate there is stronger relationship between LST and built up area and bare land. And water bodies and vegetation
area is lower temperature. However, the extension of residential was continuously increase along the transportation
lines year by year. So, the higher LST area was wider and wider overlap residential area and bare land. Now, much
research has yet to be done to understand present condition of livelihood of local people and identify vulnerable
communities and aspects of their livelihood that is vulnerable to climate change. Therefore, land development of
Yangon Mega City is rising LST and one such area in the country that is facing the devastating effects of climate
change as well as urban environment.
LIMITATION OF THIS STUDY
By using multi-temporal remotely sensed data and statistical analysis, this paper presented better results for the
spatiotemporal patterns of land use and land cover classification and land surface temperature using fewer remotely
sensed data images focusing on the relatively the Yangon Mega City and surrounding area. Thus, in future research,
synchronous data from in-situ observations should be combined with multi-temporal, long time span satellite data to
produce more accurate results for the sustainable development of Yangon Mega City.
ACKNOWLEDGEMENTS
The author gratefully acknowledges the guidance of Professor Dr. Choi Chul Uong, PE&RS lab, Spatial Information
Engineering Department, Pukyong National University, and special thanks for Mr. Ahn Hoyong, Ph D candidate,
PE&RS lab, Spatial Information Engineering Department, Pukyong National University for his valuable suggestions
during the preparation of this paper.
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